Fraud Detection in Online Banking Using Machine Learning

  • Authors

    • Noah Wright Data Engineering Lead, SAP, Germany. Author
    • Isabella Moore HR Director, Siemens, Germany. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2021PI3C9D

    Published 04-02-2021

  • Online Banking, Fraud Detection, Machine Learning, Financial Security, Random Forest, Transaction Analysis, Cybersecurity, Data Mining

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    Wright, N., & Moore, I. (2021). Fraud Detection in Online Banking Using Machine Learning. International Journal of Commerce, Finance and Digital Economy, 4(1), 01-17. https://doi.org/10.67228/3071642X/IJCFDE-2021PI3C9D

  • Abstract

    Machine Learning-based fraud detection systems provide an effective and intelligent solution for securing online banking platforms against evolving cyber threats. By analyzing transaction patterns and detecting anomalies in real time, ML algorithms such as Logistic Regression, Decision Tree, Random Forest, and XGBoost can accurately identify fraudulent activities while reducing false alarms. Experimental results indicate that ensemble models, particularly Random Forest, achieve superior detection performance. The proposed framework enhances fraud prevention, minimizes financial losses, improves customer trust, and offers a scalable and adaptive approach for modern digital banking security.

  • References

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